Random-Forest (RF) and Support Vector Machine (SVM) Implementation for Analysis of Gene Expression Data in Chronic Kidney Disease (CKD)

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

Research in cancer genomics has proliferated with the advent of microarray technologies. These technologies facilitate monitoring of thousands of genes in parallel, thus providing insight into disease subtypes and gene functions. Gene expression data obtained from microarray chips are typified by few samples and a large number of genes. Supervised classifiers such as support vector machines (SVM) have been deployed for prediction task. However, insufficient labeled data have resulted in a paradigm shift to semi-supervised learning, in particular, transductive SVM (TSVM). Analysis of gene expression data using TSVM revealed that the performance of the model degenerates in the presence of unlabeled data. We address this issue by using a representative sampling strategy which ensures safety of the classifier even in the presence of unlabeled data. We also address the issue of privacy violation when classifier is shipped to other medical institutes for analysis of shared data. We propose a safety aware and privacy preserving TSVM for classifying cancer subtypes. Performance of TSVM with SVM and accuracy loss of the proposed TSVM are also analyzed.

Support Vector Machines (SVM) are powerful machine learning techniques that are able to deal with high dimensional and noisy data. They have been successfully applied to a wide range of problems and particularly to the analysis of gene expression data. However SVM algorithms rely usually on the use of the Euclidean distance that often fails to reflect the object proximities. Several versions of the SVM have been proposed that incorporate non Euclidean dissimilarities. Nevertheless, different dissimilarities reflect complementary features of the data and no one can be considered superior to the others. In this paper, we present an ensemble of SVM classifiers that reduces the misclassification error combining different dissimilarities. The method proposed has been applied to identify cancerous tissues using Microarray gene expression data with remarkable results.

Background - In the NeoTRIPaPDL1 phase III trial, triple-negative breast cancer (TNBC) patients were randomized to receive nab-paclitaxel/carboplatin for 8 cycles (CT arm) with or without atezolizumab (CT/A arm). We previously reported that the majority of patients (65%) with PD-L1- at baseline converted to PD-L1+ (the majority with IC2/IC3) after the first cycle of treatment in the atezolizumab arm. Here we studied treatment associated changes in the molecular tumour features and immune microenvironment by PD-L1 expression. Methods – A total of 158 (56.4%) of patients enrolled in the NeoTRIPaPDL1 trial (76 and 82 from CT/A and CT arm, respectively) were included in this analysis by satisfying the following criteria: i) availability of a paired core biopsy at baseline and at day 1 of the second treatment cycle (D1C2); ii) evaluation of stromal TILs (sTILs) and staining for PD-L1 (Ventana SP142); iii) successful RNA-seq gene expression profiling. PD-L1 groups were defined as IC0/IC1 (<5% PD-L1low) vs IC2/IC3 (>=5% PD-L1high). Presence of different immune cell populations was quantified by gene expression profile deconvolution using ConsensusTME R package. Cancer hallmark gene set collection and custom signature activation status were estimated in each sample using singscore R package. Differences in score distribution were evaluated by 2-sided t-test. Results At baseline, both sTILs and immune cell signatures were upregulated in PD-L1high compared to PD-L1low tumours (p<0.001). No significant differences were found between the two treatment arms in the PD-L1high subpopulation. In the PD-L1low cases, both sTILs and immune related signatures were slightly downregulated in the CT/A arm (p<0.05), suggesting a modest unbalance among treatment arms with CT arm being slightly more inflamed than CT/A arm.At D1C2, PD-L1high tumours in the CT arm systematically had high sTILs (median=70%, range=30-90%), while PD-L1low tumours receiving CT/A had low sTILs in a significant proportion of cases (median=30%, range=0-90%; p<0.001). Similarly, at D1C2 several gene expression-estimated immune cell populations and immune-related signatures were upregulated in the CT arm compared to CT/A arm in PD-L1high tumours, with the weakest association observed for M2 macrophages (p=0.058). No tumour-related signatures were differentially expressed among the two treatment arms within groups with PD-L1high, suggesting that different treatment. modulate PD-L1 by engaging a different immune mileau instead of modulating tumor related features.Considering PD-L1low groups at D1C2, in the CT arm 21 tumors had >30% sTILs (n=21/69, 30.4%), while in the CT/A arm only 2 had sTILs >30% (2/27, 7.4%) (p<0.001). Analysis of gene expression data identified IFN-related signatures as the most upregulated in the CT compared to CT/A arm in PD-L1low cases at D1C2 (p<0.05). Conclusions Integrated dynamic analysis of PD-L1 expression and gene expression data highlighted significant treatment-specific changes of the immune landscape according to PD-L1 expression, when this biomarker is assessed during treatment. This indicates that the immune milieu associated with PD-L1 status is strongly dependent on the time of assessment in relationship to treatment received. Such observation may explain why in the NeoTRIP trial, baseline PD-L1 but not on-treatment PD-L1 was predictive of pCR in CT/A arm. In addition, our findings could have implications related to the use of PD-L1 as a predictive biomarker in pre-treated patients, especially when assessed early on during treatment. Citation Format: Maurizio Callari, Chiun-Sheng Huang, Daniel Egle, Begoña Bermejo, Claudio Zamagni, Matteo Dugo, Marc Thill, Antonio Anton, Marco Barreca, Stefania Russo, Eva Maria Ciruelos, Richard Greil, Stefania Zambelli, Balázs Gyorffy, Chanel Smart, Olivia Biasi, Pinuccia Valagussa, Giuseppe Viale, Luca Gianni, Giampaolo Bianchini. Immune milieu associated with PD-L1 status in TNBC is dependent on time of biomarker assessment and treatment received: A secondary analysis of the NeoTRIPaPDL1 trial [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-04-02.

Several studies have reported mitochondrial dysfunction in rheumatoid arthritis (RA). Many nuclear DNA (nDNA) encoded proteins translocate to mitochondria, but their participation in the dysfunction of this cell organelle during RA is quite unclear. In this study, we have carried out an integrative analysis of gene expression, protein-protein interactions (PPI) and gene ontology data. The analysis has identified potential implications of the nDNA encoded proteins in RA mitochondrial dysfunction. Firstly, by analysing six synovial microarray datasets of RA patients and healthy controls obtained from the gene expression omnibus (GEO) database, we found differentially expressed nDNA genes that encode mitochondrial proteins. We uncovered some of the roles of these genes in RA mitochondrial dysfunction using literature search and gene ontology analysis. Secondly, by employing gene co-expression from microarrays and collating reliable PPI from seven databases, we created the first mitochondrial PPI network that is specific to the RA synovial joint tissue. Further, we identified hubs of this network, and moreover, by integrating gene expression and network analysis, we found differentially expressed neighbours of the hub proteins. The results demonstrate that nDNA encoded proteins are (i) crucial for the elevation of mitochondrial reactive oxygen species (ROS) and (ii) involved in membrane potential, transport processes, metabolism and intrinsic apoptosis during RA. Additionally, we proposed a model relating to mitochondrial dysfunction and inflammation in the disease. Our analysis presents a novel perspective on the roles of nDNA encoded proteins in mitochondrial dysfunction, especially in apoptosis, oxidative stress-related processes and their relation to inflammation in RA. These findings provide a plethora of information for further research.

BackgroundSerial Analysis of Gene Expression (SAGE) is a DNA sequencing-based method for large-scale gene expression profiling that provides an alternative to microarray analysis. Most analyses of SAGE data aimed at identifying co-expressed genes have been accomplished using various versions of clustering approaches that often result in a number of false positives.Principal FindingsHere we explore the use of seriation, a statistical approach for ordering sets of objects based on their similarity, for large-scale expression pattern discovery in SAGE data. For this specific task we implement a seriation heuristic we term ‘progressive construction of contigs’ that constructs local chains of related elements by sequentially rearranging margins of the correlation matrix. We apply the heuristic to the analysis of simulated and experimental SAGE data and compare our results to those obtained with a clustering algorithm developed specifically for SAGE data. We show using simulations that the performance of seriation compares favorably to that of the clustering algorithm on noisy SAGE data.ConclusionsWe explore the use of a seriation approach for visualization-based pattern discovery in SAGE data. Using both simulations and experimental data, we demonstrate that seriation is able to identify groups of co-expressed genes more accurately than a clustering algorithm developed specifically for SAGE data. Our results suggest that seriation is a useful method for the analysis of gene expression data whose applicability should be further pursued.

Machine Learning algorithms have been widely used for gene expression data classification, despite the fact that these data have often intrinsic limitations, such as high dimensionality and a small number of examples. Few studies try to characterize to which extent these aspects can influence the performance of the classification models induced. In this paper we compute different measures characterizing the complexity of gene expression data sets for cancer diagnosis. We then investigate how these measures relate to the classification performances achieved by support vector machines, a popular Machine Learning technique usually employed in the analysis of gene expression data. The results obtained indicate that some of the complexity indices utilized are indeed successful in explaining the difficulty involved in the classification of cancer gene expression data.

ObjectiveThis study aimed to identify prognostic genes for diffuse large B-cell lymphoma (DLBCL), using bioinformatic methods.MethodsFive gene expression data sets were downloaded from the Gene Expression Omnibus database. Significance analysis of microarrays algorithm was used to identify differentially expressed genes (DEGs) from two data sets. Functional enrichment analysis was performed for the DEGs with the Database for Annotation, Visualization and Integration Discovery (DAVID). Survival analysis was performed with the Kaplan–Meier method using function survfit from package survival of R for the other three data sets. Cox univariate regression analysis was used to further screen out prognostic genes.ResultsThirty-one common DEGs were identified in the two data sets, mainly enriched in the regulation of lymphocyte activation, immune response, and interleukin-mediated signaling pathway. Combined with 47 DLBCL-related genes acquired by literature retrieval, a total of 78 potential prognostic genes were obtained. Cases from the other three data sets were used in hierarchical clustering, and the 78 genes could cluster them into several subtypes with significant differences in survival curves. Cox univariate regression analysis revealed 45, 33, and eleven prognostic genes in the three data sets, respectively. Five common prognostic genes were revealed, including LCP2, TNFRSF9, FUT8, IRF4, and TLE1, among which LCP2, FUT8, and TLE1 were novel prognostic genes.ConclusionFive prognostic genes of DLBCL were identified in this study. They could not only be used for molecular subtyping of DLBCL but also be potential targets for treatment.

BackgroundAcute myocardial infarction (AMI) is a major cause of morbidity and mortality. Disulfidptosis, a novel form of programmed cell death, has been largely unexplored in AMI. This study aims to identify disulfidptosis-related genes in AMI and assess their diagnostic potential using bioinformatics and machine learning.MethodsThe microarray datasets GSE60993 and GSE61144, associated with Acute Myocardial Infarction (AMI), were obtained from the Gene Expression Omnibus (GEO) database. Differential disulfidptosis-associated genes were identified via differential expression analysis. The disulfidptosis related genes were collected from FerrDb V2 and the differentially expressed disulfidptosis related genes were utilized to construct a Protein-Protein Interaction (PPI) network. Key genes were identified utilizing a Protein-Protein Interaction (PPI) network and plugins available in Cytoscape. The key genes were used to detect potential biomarkers by receiver operating characteristic (ROC) analysis.Next, GO and KEGG analyses, as well as correlation analysis were performed on the key genes, and potential drug molecules targeting these genes were also analyzed. At the same time, key genes further screened by Support Vector Machine (SVM), Lasso regression, as well as random forest. By intersecting the results of the three, we ended up with hub genes for AMI. The expression of these key genes was verified using external dataset GSE61144.ResultsA total of 16 differentially expressed disulfidptosis related genes were identified and these genes were mainly enriched in the pathways of “regulation of actin cytoskeleton organization”, “regulation of actin filament-based process”, “regulation of actin filament organization”, “cell cortex”, “cell leading edge”, “cadherin binding”, “actin filament bindin, and “D-glucose transmembrane transporter activity”. The top 10 hub genes ACTB, RAC1, IQGAP1, FLNB, MYL6, ABI2, DBN1, PRDX1, SLC2A1 and SLC2A3 were identified from the PPI network. Further screening using Support Vector Machine (SVM), Lasso regression and random forest, and intersecting the results of these analyses, led to the identification of DBN1, RAC1, and SLC2A3 as final hub genes in AMI. While the final key genes DBN1 and SLC2A3 were significantly differentially expressed in external dataset GSE61144 with AUC ≥ 0.7.ConclusionIn this study, we identified differentially expressed disulfidptosis related genes in blood samples from AMI patients using existing datasets. The research delved into the expression patterns and molecular mechanisms of differentially expressed disulfidptosis related genes in AMI, offering a foundation for precise AMI diagnosis and the identification of novel therapeutic targets.

Breast cancer remains a leading cause of mortality among women worldwide, with early and accurate diagnosis being critical for effective treatment. Gene expressionprofiling has emerged as a powerful tool for understanding the molecular mechanisms of cancer and for developing predictive models. However, the high dimensionality and class imbalance inherent in gene expression data pose significant challenges for developing robust predictive models.This study aims to develop and evaluate a predictive model for classifying breast cancer subtypes using high-dimensional gene expression data, addressing the challenges of class imbalance. The objective is to improve the accuracy and reliability of breast cancer subtype prediction to facilitate better diagnostic and treatment strategies.A comprehensive dataset of breast cancer gene expression profiles was utilized, comprising numerous gene expression levels across multiple samples. To address class imbalance, resampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and Random Under-sampling, were employed. The machine learning algorithms employed included Support Vector Machines (SVM), Random Forests, and Neural Networks. The algorithms were trained and evaluated using cross-validation to identify the most effective model. The performance of these models was assessed based on metrics such as accuracy, precision, recall, and F1-score. The results indicate that the use of SMOTE in combination with SVM provided the most balanced and accurate predictions, with an F1-score significantly higher than models without resampling. The Random Forest algorithm also showed promising results, particularly in handling the high-dimensionality aspect of the data.The study demonstrates that addressing class imbalance through advanced resampling techniques can significantly enhance the predictive accuracy of models trained on high-dimensional gene expression data. The findings underscore the potential of machine learning models, particularly SVMs and Random Forests, in improving breast cancer subtype classification.

Lung adenocarcinoma (LUAD) is a major challenge in oncology due to its complex molecular structure and generally poor prognosis. The aim of this study was to find diagnostic markers and therapeutic targets for LUAD by integrating differential gene expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning methods. Differentially expressed genes (DEGs) were identified through the analysis of gene expression data from the Gene Expression Omnibus (GEO) database. Ten of the gene co-expression modules constructed by WGCNA were identified, with the red module having the most significant correlation with clinical features. In addition, a machine learning model constructed based on Stepglm[backward] with the random forest algorithm achieved the highest C-index (0.999) and screened eight core genes, among which ST14 was noted for its excellent predictive ability. Single-cell RNA sequencing of the LUAD samples further analyzed the expression patterns of these genes in 29 cellular subtypes, revealing their significant association with immune cell infiltration. Of particular note, the association of ST14 with clinical prognosis, drug responsiveness, and immune infiltration was validated, while enrichment analysis further clarified its role in key biological pathways. Ultimately, the expression of the core genes was validated experimentally. This study provides new insights into the pathogenesis of LUAD, clarifies potential diagnostic markers and therapeutic targets, and provides an important basis for future clinical interventions.

Recent research has focused on incorporating biological function and pathway information into the analysis of gene expression data, partly as a means of compensating for insufficient experimental replications, low signal to noise, lack of reproducibility and/or multiple testing confounds. A Bayesian approach seems to be ideal for incorporating functional information into gene expression data analysis. In this study, we tested the feasibility of using literature derived gene relationships in a Bayesian model to analyze gene expression data. Prior distributions were constructed based on gene associations derived from the biomedical literature using Latent Semantic Indexing (LSI). The LSI model was built using more than 1 million Medline abstracts corresponding to 22,000 human and mouse genes. A key advantage of LSI is that both explicit and implicit gene relationships can be derived from the literature. Gene neighborhoods were determined using latent Gaussian Markov random fields and logistic transformation of the latent variables. We tested the procedure on a microarray dataset for interferon-stimulated genes in mouse embryonic fibroblasts. By integrating functional information from literature, Bayesian approach identified relevant genes that previously did not meet the 0.05 significance level. In comparison to a standard mixture model, spatial mixture model has more power for identifying direct and indirect interferon regulated genes. The spatial model enhanced the ranks of some genes which are known to be affected by interferon treatment, such as Nmi (NMI N-myc and STAT interactor) and ifi35 (interferon-induced protein 35). It also identified some genes that previously were ignored because of the marginal p-values, such as dpysl2, map2k1, msn, Psck5, and Il6st. Interestingly, these genes appear to be indirectly related to interferon treatment. In summary, we show that our procedure increases statistical power and produces more biologically meaningful gene lists. These results suggest that Bayesian methods which incorporate functional information from the literature may improve analysis of gene expression data.

The aim of this study was to identify key genes related to invasive ductal carcinoma (IDC) of the breast by analyzing gene expression data with bioinformatic tools. Microarray data set GSE31138 was downloaded from Gene Expression Omnibus, including 3 breast cancer tissue samples and 3 normal controls. Differentially expressed genes (DEGs) between breast cancer and normal control were screened out (FDR < 0.05 and |logFC| > 2). Coexpression between genes was examined with String, and a network was then constructed. Relevant pathways and diseases were retrieved with KOBAS. A total of 56 DEGs were obtained in the IDC of the breast compared with normal controls. A gene coexpression network including 27 pairs of genes was constructed and all the genes in the network were upregulated. Further study indicated that most of the genes in the coexpression network were enriched in ECM-receptor interaction (COL4A2, FN1, and HMMR) and nucleotide excision repair (CETN2 and PCNA) pathways, and that the most significantly related disease was autoimmune lymphoproliferative syndromes. A number of DEGs were acquired through comparative analysis of gene expression data. These findings are beneficial in promoting the understanding of the molecular mechanisms in breast cancer. More importantly, some key genes were revealed via gene coexpression network analysis, which could be potential biomarkers for IDC of the breast.

The Impact of APOL1 on Chronic Kidney Disease and Hypertension.

Cancer survival classification using integrated data sets and intermediate information.